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An ensemble of single multiplicative neuron models for probabilistic prediction

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An ensemble of single multiplicative neuron models for probabilistic prediction. / Yolcu, Ufuk; Jin, Yaochu; Egrioglu, Erol.

2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. Institute of Electrical and Electronics Engineers Inc., 2017. 7849975 (2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Yolcu, U, Jin, Y & Egrioglu, E 2017, An ensemble of single multiplicative neuron models for probabilistic prediction. in 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016., 7849975, 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, Institute of Electrical and Electronics Engineers Inc., 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, Athens, Greece, 6/12/16. https://doi.org/10.1109/SSCI.2016.7849975

APA

Yolcu, U., Jin, Y., & Egrioglu, E. (2017). An ensemble of single multiplicative neuron models for probabilistic prediction. In 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 [7849975] (2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2016.7849975

Vancouver

Yolcu U, Jin Y, Egrioglu E. An ensemble of single multiplicative neuron models for probabilistic prediction. In 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. Institute of Electrical and Electronics Engineers Inc. 2017. 7849975. (2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016). https://doi.org/10.1109/SSCI.2016.7849975

Author

Yolcu, Ufuk ; Jin, Yaochu ; Egrioglu, Erol. / An ensemble of single multiplicative neuron models for probabilistic prediction. 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. Institute of Electrical and Electronics Engineers Inc., 2017. (2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016).

Bibtex

@inproceedings{534df7322c234ddeb8ceb1bab47a38f5,
title = "An ensemble of single multiplicative neuron models for probabilistic prediction",
abstract = "Inference systems basically aim to provide and present the knowledge (outputs) that decision-makers can take advantage of in their decision-making process. Nowadays one of the most commonly used inference systems for time series prediction is the computational inference system based on artificial neural networks. Although they have the ability of handling uncertainties and are capable of solving real life problems, neural networks have interpretability issues with regard to their outputs. For example, the outputs of neural networks that are difficult to interpret compared to statistical inference systems' outputs that involve a confidence interval and probabilities about possible values of predictions on top of the point estimations. In this study, an ensemble of single multiplicative neuron models based on bootstrap technique has been proposed to get probabilistic predictions. The main difference of the proposed ensemble model compared to conventional neural network models is that it is capable of getting a bootstrap confidence interval and probabilities of predictions. The performance of the proposed model is demonstrated on different time series. The obtained results show that the proposed ensemble model has a superior prediction performance in addition to having outputs that are more interpretable and applicable to probabilistic evaluations than conventional neural networks.",
keywords = "bootstrap technique, ensemble, probabilistic prediction, single multiplicative neuron model, time series prediction",
author = "Ufuk Yolcu and Yaochu Jin and Erol Egrioglu",
year = "2017",
month = feb,
day = "13",
doi = "10.1109/SSCI.2016.7849975",
language = "English",
series = "2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016",
note = "2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 ; Conference date: 06-12-2016 Through 09-12-2016",

}

RIS

TY - GEN

T1 - An ensemble of single multiplicative neuron models for probabilistic prediction

AU - Yolcu, Ufuk

AU - Jin, Yaochu

AU - Egrioglu, Erol

PY - 2017/2/13

Y1 - 2017/2/13

N2 - Inference systems basically aim to provide and present the knowledge (outputs) that decision-makers can take advantage of in their decision-making process. Nowadays one of the most commonly used inference systems for time series prediction is the computational inference system based on artificial neural networks. Although they have the ability of handling uncertainties and are capable of solving real life problems, neural networks have interpretability issues with regard to their outputs. For example, the outputs of neural networks that are difficult to interpret compared to statistical inference systems' outputs that involve a confidence interval and probabilities about possible values of predictions on top of the point estimations. In this study, an ensemble of single multiplicative neuron models based on bootstrap technique has been proposed to get probabilistic predictions. The main difference of the proposed ensemble model compared to conventional neural network models is that it is capable of getting a bootstrap confidence interval and probabilities of predictions. The performance of the proposed model is demonstrated on different time series. The obtained results show that the proposed ensemble model has a superior prediction performance in addition to having outputs that are more interpretable and applicable to probabilistic evaluations than conventional neural networks.

AB - Inference systems basically aim to provide and present the knowledge (outputs) that decision-makers can take advantage of in their decision-making process. Nowadays one of the most commonly used inference systems for time series prediction is the computational inference system based on artificial neural networks. Although they have the ability of handling uncertainties and are capable of solving real life problems, neural networks have interpretability issues with regard to their outputs. For example, the outputs of neural networks that are difficult to interpret compared to statistical inference systems' outputs that involve a confidence interval and probabilities about possible values of predictions on top of the point estimations. In this study, an ensemble of single multiplicative neuron models based on bootstrap technique has been proposed to get probabilistic predictions. The main difference of the proposed ensemble model compared to conventional neural network models is that it is capable of getting a bootstrap confidence interval and probabilities of predictions. The performance of the proposed model is demonstrated on different time series. The obtained results show that the proposed ensemble model has a superior prediction performance in addition to having outputs that are more interpretable and applicable to probabilistic evaluations than conventional neural networks.

KW - bootstrap technique

KW - ensemble

KW - probabilistic prediction

KW - single multiplicative neuron model

KW - time series prediction

U2 - 10.1109/SSCI.2016.7849975

DO - 10.1109/SSCI.2016.7849975

M3 - Conference contribution/Paper

AN - SCOPUS:85016049289

T3 - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

BT - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

Y2 - 6 December 2016 through 9 December 2016

ER -